Abstract

Ultra-high field MRI (≥7 T) has recently shown great sensitivity to depict patterns of tissue microarchitecture. Moreover, recent studies have demonstrated a dependency between T2* and orientation of white matter fibers with respect to the main magnetic field B0. In this study we probed the potential of T2* mapping at 7 T to provide new markers of cortical architecture. We acquired multi-echo measurements at 7 T and mapped T2* over the entire cortex of eight healthy individuals using surface-based analysis. B0 dependence was tested by computing the angle θz between the normal of the surface and the direction of B0, then fitting T2*(θz) using model from the literature. Average T2* in the cortex was 32.20+/−1.35 ms. Patterns of lower T2* were detected in the sensorimotor, visual and auditory cortices, likely reflecting higher myelin content. Significantly lower T2* was detected in the left hemisphere of the auditory region (p<0.005), suggesting higher myelin content, in accordance with previous investigations. B0 orientation dependence was detected in some areas of the cortex, the strongest being in the primary motor cortex (ΔR2*=4.10 Hz). This study demonstrates that quantitative T2* measures at 7 T MRI can reveal patterns of cytoarchitectural organization of the human cortex in vivo and that B0 orientation dependence can probe the coherency and orientation of gray matter fibers in the cortex, shedding light into the potential use of this type of contrast to characterize cyto-/myeloarchitecture and to understand the pathophysiology of diseases associated with changes in iron and/or myelin concentration.

The aims of this study were: (i) to acquire high-resolution multi-echo measurements at 7 T in order to map T2* over the entire cortex of healthy individuals using a surface-based analysis; (ii) to test the presence of a dependency between T2* and the orientation of the cortical surface relative to B0, and to assess whether this dependency underlies patterns of cyto- and myeloarchitectural organization of the human cortex.

Material and methods

Acquisition

Healthy subjects (N=8, age=41+/−8 years) were recruited. General exclusion criteria were significant medical, psychiatric, or neurologic history. The Institutional Review Board of the Massachusetts General Hospital approved all experimental procedures of the study, and written informed consent was obtained from each subject.

Processing

Correction for background field gradients

Despite careful B0 shimming, some inhomogeneities remained, especially in lower brain regions and in regions of air/tissue interface (e.g., close to the sinuses). These inhomogeneities induced background field gradients within each voxel, resulting in shorter T2* decay (Yablonskiy and Haacke, 1994) and therefore underestimation of T2* (Fernández-Seara and Wehrli, 2000; Peters et al., 2007). When anisotropic voxel is used, the slice-select direction (Z) is particularly sensitive to these background gradients (Frahm et al., 1988). This effect can be compensated by correcting each T2*-weighted signal according to the formulation introduced in Yablonskiy and Haacke (1994), which expresses the signal S(TE) in the presence of background field gradient:

S(TE)=S0⋅exp(−TET2∗)⋅∣sinc(γ⋅G⋅Δz⋅TE2)∣

(1)

where S0 is the signal strength at TE=0, γ is the Larmor frequency, G is the strength of a constant field gradient in the slice direction and Δz is the slice thickness. We corrected the signal S(TE) by first estimating the gradient frequency field along Z: ΔBz = γ.G.Δz and then dividing the signal S(TE) by the sinc term in Eq. (1), as done in Fernández-Seara and Wehrli (2000) and Peters et al. (2007). Estimation of the gradient frequency field ΔBz was performed as follows: after unwrapping the phase evolution at each voxel, the resonance frequency was calculated by fitting the slope of the phase versus echo time TE using a linear least-square approach. The frequency map was then downsampled by 2 and fitted using a 3D polynomial regression model (3rd order). The frequency gradient ΔBz was calculated analytically by deriving the polynomial expression along Z.

Parameters T2* and S0 were initialized from linear least square fitting of log(Scorr) versus TE. Voxels with poor goodness of fit from the nonlinear estimation were excluded from further analysis (adjusted R2<0.8).

Correction for gradient non-linearities

T2* data were corrected for gradient non-linearity using a 3D deformation field calculated from the specifications of the Siemens AC84 head gradient, and the image data was resampled using trilinear interpolation.

Surface-based analysis

Cortical surface models were reconstructed from 3 T image data using FreeSurfer (http://surfer.nmr.mgh.harvard.edu/). Each of the two 7 T FLASH slabs was registered to the 3 T surface using a boundary-based registration technique (Greve and Fischl, 2009) with 9 degrees of freedom. The registration to the surface was achieved using the average of the first 4 echoes (6.34–15.94 ms), empirically found to be an acceptable compromise between SNR, white/gray matter contrast, and through-plane susceptibility artifacts. Registered slabs were then concatenated using FreeSurfer tools. For more details on the registration, the reader is referred to Cohen-Adad et al. (2011). T2* was sampled along the midline between the pial and the white matter surface (50% depth) across the entire cortical hemisphere. Each individual T2* map was normalized to the ‘fsaverage’ template surface available in FreeSurfer, then averaged across subjects and smoothed along the surface using a 3 mm FWHM Gaussian kernel. To identify regions of lower/higher T2*, one-sample Student’s t-test was performed on a vertex-by-vertex basis between the T2* distribution (across subjects) and the median T2* (across the cortex). Maps of –log(p) were generated (threshold at p = 0.0001 with FDR correction). To assess inter-hemispheric differences in T2*, two-sample Student’s t-test was performed in each sub-region of the cortex defined by the PALS-B12 Brodmann atlas available in FreeSurfer (Van Essen, 2005).

Cortical thickness was estimated for each subject based on the 3 T multi-echo MPRAGE using FreeSurfer (Fischl and Dale, 2000). The resulting thickness map was spatially normalized to the fsaverage template and then averaged across subjects for comparison with the T2* maps.

B0 orientation dependence

The effect of B0 orientation on T2* contrast was investigated on the basis of the radially and tangentially oriented fibers penetrating into the cortex and their potential effect on T2*. Instead of using diffusion tensor information to relate orientation of B0 and orientation of fiber bundles (Bender and Klose, 2010; Cherubini et al., 2009; Denk et al., 2011; Lee et al., 2011), we related the orientation of B0 to the vector normal to the cortical surface (Fig. 1). To probe the existence of any B0 orientation dependence, it was necessary to sample the tissue at various orientations within the bore. Here, instead of rotating the head of participants as done previously (Bender and Klose, 2010; Wiggins et al., 2008), we took advantage of the convoluted nature of the human cortical folding pattern to obtain a wide range of angles between B0 and any intracortical fibers that are consistently aligned with the direction of the cortical surface (i.e., radial or tangential). The angle θz between the main B0 field and the vector normal to the mid surface was computed on a vertex-by-vertex basis. R2* (inverse of T2*) was then plotted against θz[0,π/2) and a regression was conducted within each region of the PALS-B12 Brodmann atlas. The rationale for fitting the R2*(θz) data within Brodmann areas is that we can expect the local cyto/myeloarchitecture, which varies across the Brodmann areas, to drive the orientation dependence. The fitting equation is:

R2∗=c0+c1⋅sin(2θz+φ0)

(3)

where c0 represents the background portion of R2* that is not affected by B0 orientation, c1 represents the portion of R2* that does depend on B0 orientation, and 0 is a phase offset term. This equation is derived from the model used by Lee et al. (2011) without the suggested sin(4θz) term (i.e., Eq. (3) corresponds to the isotropic susceptibility model). The reason for not including the 4θz dependence was that susceptibility anisotropy (Lee et al., 2010b; Liu, 2010) has not been assessed in the gray matter. Another argument for choosing the Lee model is that, here, no assumption could be made on the orientation of the fibers with respect to B0, therefore a phase parameter was required in the sinusoidal term (e.g., to account for tangential versus radial fibers). Fitting was achieved in Matlab (The MathWorks, Inc., USA) using robust non-linear least squares fitting (bisquare weights method). Confidence bounds at 95% were calculated. The dependency of T2* toward B0 orientation was assessed by the ȣc1ȣ parameter, which scales the sinusoidal term in Eq. (3). Variation of R2* was computed as ΔR2*=2 c1.

The effect of the main magnetic field (B0) orientation on T2* contrast was investigated on the basis of the radially and tangentially oriented fibers penetrating into the cortex. T2* was sampled at 50% depth across the whole cortex, surface-smoothed with...

Results

T2*-weighted data were successfully acquired and T2* estimated in all subjects (N = 8). Fig. 2 shows T2*-weighted data with the fitted values and the adjusted R2 statistics (capturing the goodness-of-fit of the T2* model) in a representative subject.

A. First (6 ms) and last echo (45 ms) of a FLASH T2*-weighted image in a representative subject. B. T2* map computed voxelwise from 12 echoes using least-squares fitting of the log of the data vs. echo time. C. Adjusted R2 assessing the goodness of T...

The mean T2* over the cortex of all individuals was 32.20+/−1.35 ms (this average does not include the medial wall). Fig. 3A shows the average T2* mapped on the inflated surface. Regions with lower T2* are noticeable in the primary sensorimotor, occipital and temporal cortices. The ‘strip’ pattern of lower T2* in the sensorimotor region is centered along the central sulcus and extends across the pre- and post-central gyri. Missing or low T2* values located in the lower brain are due to poor coverage or through-slice susceptibility artifacts caused by large B0-inhomogeneities.Fig. 3B shows results of the one-sample Student’s t-test that assessed regional T2* differences compared to the T2* median across the cortex (threshold at p<0.0001 with correction for false discovery rate, FDR). As observed on the T2* map, lower T2* was detected in the primary sensorimotor (“1”), visual (“2”) and auditory areas (“3”). Conversely, higher T2* was detected in the superior and middle frontal sulci (“4”) and in the anterior and posterior cingulate (“5”).

A. T2* maps sampled at 50% depth, surface-smoothed with 5 mm kernel, normalized to the fsaverage template, averaged across subjects (N=8) and displayed on the inflated surface. B. Map of significant differences between T2* in each vertex and the median...

Fig. 4 shows the left lateral view of the mean T2* map with an overlay of the Brodmann areas predicted from the PALS-B12 atlas, the cortical thickness map computed from all subjects and the curvature map representing distribution of sulci and gyri. ‘Strips’ of lower T2* are noticeable on both sides of the central sulcus (indicated by the arrow) and correspond to a relatively large cortical thickness (2.5–3 mm). Conversely, the posterior bank of the central sulcus (area 3b/1), which has thinner cortex (1.5–2 mm), is associated with slightly higher T2*, which could result from partial volume effects with the cerebrospinal fluid (CSF). A measure of spatial correlation was computed between the T2* map and the cortical thickness map using the Spearman’s coefficient, yielding ρ = 0.0046 (degrees of freedom = 275,929). Distribution of T2* resembles subdivisions of the Brodmann areas, with homogeneous regions of lower T2* in areas such as BA1 (primary somatosensory cortex), BA4 (primary motor cortex), BA19 (visual cortex) and BA42 (auditory association cortex). The bar graph shows T2* values averaged across subjects for each hemisphere. Most areas show fairly good reproducibility across hemispheres, however significant differences were detected in areas BA22, BA37 (p<0.05) and BA42 (p<0.005). Significantly lower T2* in the left hemisphere of the latter area (BA42) is of particular interest as it is in accordance with previous observations based on quantitative T1 mapping (Sigalovsky et al., 2006) and will be further discussed.

Lateral left view of the averaged T2* map with an overlay of the PALS-B12 Brodmann atlas, which represents regions with specific cytoarchitectonics. The thickness map was computed from the 3 T MPRAGE data and averaged across subjects. The curvature map...

Fig. 5A shows plots of R2*(θz) in two regions of the cortex defined by the PALS-B12 atlas (BA2 and BA4). These plots result from a concatenation of data from both hemispheres and all subjects (N=8). Variation of R2*, expressed as ΔR2*=2 c1, was 0.48 Hz in BA2 and 4.10 Hz in BA4. This result suggests greater orientation dependence in BA4 compared to BA2 (by a factor 8.5). Fig. 5B shows a map of ΔR2* across 29 regions of the PALS-B12. Quantitative values for all regions are also displayed as a bar graph (error bars represent the 95% confidence bounds of the fit). ΔR2* ranges between 0 and 4.1 Hz. Highest ΔR2* values were detected in BA4, BA3 (primary somatosensory cortex), BA17 (V1, primary visual cortex), BA42 (auditory association cortex), BA44/45 (Broca’s area) and particularly low ΔR2* was observed in BA2. Fig. 5C shows independent estimates of ΔR2* in each hemisphere of the cortex. Important inter-hemispheric differences were observed in BA2-3, BA7, BA9-11, BA18-19, BA23, BA42 and BA45-47 (ratio >2).

A. R2* versus θz in two regions of the cortex defined by the Brodmann atlas (BA2 and BA4). For visualization, R2* values were averaged by bins of 2 θz across all subjects (hence 45 bins in total). Standard deviation is displayed as light...

Discussion

T2* was estimated in the cerebral cortex in vivo in healthy individuals from multi-echo measurements at 7 T and mapped into a common surface space. Lower T2* was detected in some regions of the cortex, such as in the sensorimotor, visual and auditory cortices. B0 orientation dependence of T2* was detected in the cortex, shedding light into the potential use of this contrast for studying myeloarchitecture.

Origin of T2* contrast

T2* contrast in the cortex has been associated with non-heme iron stored in ferritin (Fukunaga et al., 2010; Haacke et al., 2005). Given the co-localization of iron and myelinated fibers, the presence of myelin also correlates with T2* contrast (Connor et al., 1990). Water trapped in myelin has low T2 relaxation time (~20 ms at 3 T) as opposed to the water within intra/extracellular compartments (~80 ms at 3 T), which is another cause for shorter T2* in the presence of myelin (Du et al., 2007). T2* can also be modulated by the local concentration of deoxyhemoglobin in venous blood (Duyn et al., 2007; Haacke et al., 2005) and by calcium concentration–a diamagnetic ion present at synaptic sites (Marques et al., 2009). For more information on the origin of the T2* contrast, the reader is referred to references (Fukunaga et al., 2010; Haacke et al., 2005).

T2* variations in the cortex

The averaged T2* in the cortex was 32.20+/−1.35 ms(mean+/−SD). In comparison, Peters et al. (2007) found 33.2+/−1.3 ms. Although relatively small, this discrepancy might come from the possibly different regions of interest.

Lower T2* was notably detected in BA1 (primary somatosensory cortex), BA4 (primary motor cortex), BA19 (visual cortex) and BA42 (auditory association cortex), relative to the rest of the cortex. Similar spatial patterns were also observed in the marmoset (Bock et al., 2011) and in humans using quantitative T1 (Fischl et al., 2004; Geyer et al., 2011; Weiss et al., 2011) and measurements of the ratio between T1- and T2-weighted images (Glasser and Van Essen, 2011) and are likely due to an increase of myelin density in these regions.

Lower T2* in the auditory associative cortex (BA42) could also be explained by greater myelin density, in accordance with a previous study reporting lower T1 (Sigalovsky et al., 2006) and lower T2-weighted signal (Yoshiura et al., 2000) in the Heschl’s gyrus. In addition, T2* in BA42 of the left hemisphere was significantly lower than that in the right hemisphere (p<0.005). In the same article by Sigalowsky et al., inter-hemispheric difference in the auditory cortex was also detected, with lower T1 in the left hemisphere. Lower T1 and lower T2* are both consistent with greater gray matter myelination in left auditory cortex. These observations are also supported by previous ex vivo data (Anderson et al., 1999; Buxhoeveden et al., 2001; Galuske et al., 2000; Hutsler and Gazzaniga, 1996; Seldon, 1981a, b, 1982) and suggest that higher myelination may be a substrate for the left hemisphere’s specialized processing of speech, language, and rapid acoustic changes. The two other areas showing significant inter-hemispheric differences (BA22, BA37, p<0.05) are located in lower regions of the brain that show high susceptibility artifacts, therefore no conclusion could be drawn on the genuineness of this difference.

It is possible that the cellular organization (orientation, density) also contributed to the T2* singularities observed in some areas of the cortex, given the remarkable spatial correspondence of the T2* map with the Brodmann atlas (Van Essen, 2005), although at this point it is still difficult to disentangle cellular organization from iron and myelin contribution to the T2* contrast (Fukunaga et al., 2010). This ambiguity highlights the need to perform further measurements based on different biophysical properties, such as quantitative magnetization transfer (Levesque and Pike, 2009), diffusion tensor imaging (Basser and Pierpaoli, 1996) or myelin water mapping from T2 relaxometry (MacKay et al., 2006). Each of these metrics shows high specificity toward myelin content, therefore combining them together into a common space using surface-based analysis could help studies of cortical parcellation and disentangle the effect of cytoversus myeloarchitecture in the generation of the T2* contrast.

Cortical thickness

A similar strip pattern along the central sulcus was observed when comparing the T2* map and the cortical thickness map. Notably, the posterior bank of the central sulcus, which has thinner cortex (1.5–2 mm), was associated with slightly higher T2*. Conversely, area V1 exhibited both thinner cortex and lower T2*. While some regions exhibiting low or high T2* corresponded to regions of thick or thin cortex, the two maps are largely independent. Spearman’s coefficient showed low spatial correlation between the two maps (ρ=0.0046). Thus, we can conclude that the measured T2* is not simply reflecting cortical thickness differences (due to, e.g., partial volume effects).

B0 orientation dependence

The effect of the main magnetic field (B0) orientation on T2* contrast was investigated, in line with previous studies reporting B0 dependence of T2* magnitude and phase with respect to the orientation of fibers bundles in the white matter (Bender and Klose, 2010; Cherubini et al., 2009; Denk et al., 2011; Lee et al., 2010b; Liu, 2010; Sati et al., 2012; Schäfer et al., 2009b; Wiggins et al., 2008) or at the white/gray matter interface (Schäfer et al., 2009a). This dependence is thought to arise from highly coherent and anisotropic molecular structure of the phospholipid bilayer of the myelin sheath, which has been shown to exhibit susceptibility in vitro (Boroske and Helfrich, 1978; Scholz et al., 1984; Speyer et al., 1987). Here, given the limited amount of information on the orientation of fibers in the cortex, we explored this B0 dependence by looking at the angle between the cortical surface and B0. We assumed that fibers exhibit similar orientation relative to the cortical surface, within regions that are anatomically distinct on the basis of their cytoarchitecture (as defined by Brodmann areas). Our results show variable B0 dependencies across the cortex, the highest dependency being in the primary motor cortex (BA4) where orientation-dependent variation of R2* was 4.10 Hz (fitted using the isotropic model). In comparison, Lee et al. (2011) found ΔR2*=6.44 Hz+/−0.15 in the corpus callosum at 7 T (fitted using the anisotropic model). Possible explanations for a lower ΔR2* in our experiment are that fiber bundles in the corpus callosum are more dense than that in the cortex and exhibit higher coherency, therefore creating a greater bulk susceptibility, which in turn yields greater orientation dependence with respect to B0. This argument is supported by the study of Sati et al., in which no significant change of R2* was found in the gray matter cortex of marmosets imaged in sphinx and supine position, whereas strong dependence was observed in the optic radiation in the white matter (Sati et al., 2012). The discrepancy between our study and the study from Sati et al. suggests that subtle R2* changes related to B0 orientation may only be seen in the gray matter by multiple sampling of the angle between cortical fibers and B0, combined with subsequent modeling of the R2* dependency using cylindrical susceptibility perturbers (Lee et al., 2010a). Comparing our results with 3 T measurements in the in vivo white matter, Bender et al. found ΔR2*=2.68 Hz (Bender and Klose, 2010) and Denk et al. found ΔR2*=1.65 Hz (Denk et al., 2011). While these two studies looked at R2* variation in white matter, the higher cortical ΔR2* found here likely comes from the higher field strength (7 T versus 3 T). ΔR2* estimated independently in each hemisphere seemed fairly reproducible in some areas, although important differences were observed in BA2-3, BA7, BA9-11, BA18-19, BA23, BA42 and BA45-47 (ratio >2 between left and right hemisphere). This variability can be accounted by the relatively low number and uneven distribution of θz values sampled in some areas; hence, reducing the number of θz values by a factor of two further reduced the robustness of the fit. For example, if in one Brodmann area we observe a cluster of θz values,the corresponding area in the other hemisphere may exhibit a different pattern of θz due to differences in the geometry of the folding patter, resulting in a different bias in the R2* fit calculated for each hemisphere. Another source of inter-hemispheric variability is the presence of B0 inhomogeneities in lower brain regions and close to the sinuses, which yielded unreliable estimate of R2*. A third explanation is that the actual microstructure might be different in the two hemispheres of a given Brodmann area, as been shown, e.g., in the auditory cortex (Sigalovsky et al., 2006).

Regions that were particularly affected by the orientation dependence were BA4 (primary motor cortex), BA3 (primary somatosensory cortex), BA17 (primary visual cortex V1), BA42 (auditory association cortex) and BA44/45 (Broca’s area). Interestingly, those regions are also known to be heavily myelinated (Barbier et al., 2002; Duyn et al., 2007; Sigalovsky et al., 2006). In addition to myelin content, we might hypothesize that myelinated fibers are coherently oriented in these areas, hence yielding the orientation dependence reported in previous white matter studies. To verify this hypothesis, we compared our map of B0 dependence (ΔR2*) with a map of fractional anisotropy (FA) generated from 1 mm isotropic diffusion tensor imaging (DTI) data acquired at 3 T in six other healthy subjects (McNab et al., 2011). FA is a measure of the anisotropy of water diffusion, and sometimes indicates the level of orientational coherence within a bundle of myelinated fibers (Beaulieu, 2002). FA was mapped along the mid-surface of each individual, normalized to the same fsaverage template and averaged across subjects. Fig. 6 shows the ΔR2* map and the FA map with an overlay of the PALS-B12 Brodmann areas. Higher ΔR2* (i.e., higher B0 dependence) is associated with higher FA in BA4, and lower B0 dependence is associated with lower FA in BA2. Note that some regions show high FA without systematic higher B0 dependence. This could be explained by the methodology that was used here: FA was estimated voxelwise and then mapped on the surface, whereas ΔR2* was estimated for the whole Brodmann area (including thousands of vertices). Hence, it is possible that within each vertex fibers showed high coherence and low amount of crossings (hence high FA), but within some Brodmann areas the orientation of fibers may have been different, therefore yielding low ΔR2*.

Effect of blood vessels

Radially penetrating vessels may have influenced T2* dependence with respect to B0 (Petridou et al., 2010). This effect may have been emphasized due to the anisotropic shape of our voxel size (Denk et al., 2011). However, the venous blood volume fraction is only about 3% in the human cortex (Buxton et al., 2004), suggesting that the contribution of blood vessels to the B0 dependency is marginal (Lee et al., 2010a; Petridou et al., 2010). Hence, we think that blood vessel only marginally contributed to the B0 dependence in the cortex, and that the primary reason for this dependence was myeloarchitecture features. Future studies should be conducted to investigate the contribution of blood vessels to the B0 dependence, e.g., studies employing carbogen – a mixture of carbon dioxide and oxygen gas that increases blood flow and therefore reduces deoxyhemoglobin concentration.

Limitations and future studies

We used anisotropic voxels in order to acquire the entire cortex in a reasonable scan time while maintaining high in-plane resolution (300×300 μm). Anisotropic voxels suffer from more severe partial volume effects in the through-plane direction. Thus cortical regions parallel to the image slice are worst affected and may also show a larger effect of coherently oriented cortical veins. In future, EPI-based multi-echo measurements (Zwanenburg et al., 2011) may accelerate acquisition enough to allow use of more isotropic voxels, provided that distortion correction is applied as required.

One specific application of B0 dependence analysis is the exploration of fiber orientation within the cortex, previously described using DTI (Anwander et al., 2010; McNab et al., 2011). While B0 orientation dependence in the white matter can potentially help to study features of axonal organization, we believe that studying B0 orientation dependence in the gray matter potentially has some relevance, as it might reveal new features of cortical fiber distribution and monitor re-myelination in diseases (e.g., multiple sclerosis).

Apart from these possible applications, B0 dependence in the cortex has some impact in relaxometry studies, where T2* is used as a biomarker or to develop parcellation methods. As demonstrated here, T2* is influenced by the angle between cortical fibers and B0 orientation. Hence, it is possible that spurious apparent boundaries and/or apparent change in tissue contrast within a homogeneous region (on the basis of its cellular organization) would result from a pure B0 orientation dependence effect. For instance, the recent study of Sati et al. showed drastic R2* change within the optic radiation in marmosets images in supine versus sphinx position (Sati et al., 2012), and other studies showed changes in the human pyramidal tract (Bender and Klose, 2010) and corpus callosum (Wiggins et al., 2008) depending on the orientation of the head. Future studies should aim at developing methods to correct for this orientation dependence, allowing to estimate a more intrinsic T2* value (i.e., not depending on subject’s orientation in the scanner). One possibility would be to estimate the angle between fiber bundles and B0 from DTI measurements, and then derive a spatial map of B0 dependence associated with a correcting factor to be applied to T2* measurements. This correcting factor could be calculated from models of field perturbations (Schäfer et al., 2009a; Wharton et al., 2009; Yablonskiy and Haacke, 1994). Although more challenging, robust and precise in vivo measurements of fiber bundles orientation in the cortex were shown to be feasible thanks to the use of highly parallelized phased-array receive coils (McNab et al., 2011) and increased magnetic field strength (Heidemann et al., 2010).

Acknowledgments

We thank Drs. Doug Greve, Bruce Fischl and Kawin Setsompop for helpful discussions and Dr. Boris Keil for helping with the 7 T coil. This work was supported by the National Multiple Sclerosis Society [FG 1892A1/1 to J.C-A], [Grant 4281-RG-A-1 to C.M.], by the National Center for Research Resources [P41-RR14075, and the NCRR BIRN Morphometric, Project BIRN002, U24 RR021382] and by the NIH [Human Connectome Project, U01MH093765].